kubeml 0.1.4

Python tools for training Neural Networks with KubeML

KubeML

KubeML provides wrappers and tools that allow the interaction with user code written in PyTorch with the distributed training and serving functionality offered by KubeML

Installing

Install and update using pip

pip install kubeml

Usage

The main functionality offered is in the shape of Models and Datasets. A KubeDataset is a convenience wrapper over a torch dataset which, like when using torch, users extend with their own functionality to adapt to their data. A simple example of how to create a dataset to train with KubeML is seen below.

The Dataset class

from kubeml import KubeDataset
from torchvision import transforms

class ExampleDataset(KubeDataset):

    def __init__(self, transform: transforms = None):
        super(ExampleDataset, self).__init__(dataset="mnist")
        self.transform = transform

    def __getitem__(self, index):
        x = self.data[index]
        y = self.labels[index]

        if self.transform:
            return self.transform(x), y.astype('int64')
        else:
            return x, y.astype('int64')

    def __len__(self):
        return len(self.data)

The user only needs to provide to the constructor the name of the dataset as was uploaded to the KubeML storage, the dataset will take care of fetching only the corresponding minibatches of data so that the network can be trained with a model parallel approach.

As with a normal torch dataset, the user must implement the __getitem__ and __len__ methods to iterate over the dataset. The dataset exposes two member variables:

1. data Holds the features used as input to the network
2. labels Holds the output labels

Both are saved as numpy arrays.

The Model class

The other main component is the model class. This class abstracts the complexity of distributing the training among multiple workers, nodes and GPUs. The constructor only takes a torch model as a parameter. The user only needs to implement the unimplemented methods of the class, train, infer, validate and init with the behavior they want from the network.

The Kubenet exposes the args member variable which holds the arguments used by KubeML such as batch size, learning rate... chosen by the user, which can be accessed from the training methods.

from kubeml import KubeModel
import torch
import torch.nn as nn
import numpy as np
from torch.utils import data
from torch import optim
from torchvision import transforms

class KubeNet(KubeModel):

    def __init__(self, network: nn.Module):
        super().__init__(network)

    # Train trains the model for an epoch and returns the loss
    def train(self, model: nn.Module) -> float:
        raise NotImplementedError

    # Validate validates the model on the test data and returns a tuple
    # of (accuracy, loss)
    def validate(self, model: nn.Module) -> Tuple[float, float]:
        raise NotImplementedError

    # Infer receives the data points or images as a list and returns 
    # the predictions of the network
    def infer(self, model: nn.Module, data: List[Any]) -> Union[torch.Tensor, np.ndarray, List[float]]:
        raise NotImplementedError

    # Init initializes the model in a particular way
    def init(self, model: nn.Module):
        raise NotImplementedError

An example implementation of the init and train functions can be done as follows

    # Train trains the model for an epoch and returns the loss
    def train(self, model: nn.Module) -> float:

        # Set the device as GPU, create the Example KubeDataset
        # and use a torch dataloader with the 
        device = torch.device("cuda")
        dataset = ExampleDataset()
        train_loader = data.DataLoader(dataset, batch_size=self.args.batch_size)
        optimizer = optim.Adam(model.parameters(), lr=self.args.lr)

        model = model.to(device)

        model.train()
        loss = None
        for batch_idx, (data, target) in enumerate(train_loader):
            data, target = data.to(device), target.to(device)
            optimizer.zero_grad()
            output = model(data)

            loss = F.nll_loss(output, target)
            loss.backward()

            optimizer.step()

        return loss.item()

    # Intialize the network as a pytorch model
    def init(self, model: nn.Module):
        def init_weights(m: nn.Module):
            if isinstance(m, nn.Conv2d):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0.01)
            if isinstance(m, nn.Linear):
                nn.init.xavier_uniform_(m.weight)
                nn.init.constant_(m.bias, 0.01)

        model.apply(init_weights)

Writing the training function

At the moment of creating a serverless function which will serve as a worker for the model training process, the steps are simple, simply write the code initializing the network in the main method of the function, and call start on the KubeML model.

def main():
    # Create the PyTorch Model
    net = Net()
    # create the KubeML model with the network as parameter
    kubenet = KubeNet(net)
    return kubenet.start()